This is an R Markdown document themed with {bslib} package. {bslib} makes it easy to customize the main colors and fonts of a html_document, flexdashboard::flex_dashboard, shiny::fluidPage(), or more generally any website that uses Bootstrap for styling. The theme parameter in the yaml front-matter of this Rmd document describes a bslib::bs_theme() object, which provides access to 100s of theming options (via its ... argument) in addition to the main options demonstrated here (e.g., bg, fg, primary, etc).
This particular example uses {bslib}’s default Bootstrap version (which, at the time of writing, is Bootstrap 5). However, if reproducibility is important, it’s recommended that you “lock-in” the version by adding version: 5 to the theme definition.
When running this document with {thematic} installed, the thematic::thematic_rmd(font = "auto") effectively translates theme (CSS) settings to new global theming defaults for {ggplot2}, {lattice}, and {base} R graphics:
library(ggplot2)
ggplot(mpg, aes(displ, hwy)) +
geom_point() + geom_smooth() |>
suppressMessages()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
lattice::show.settings()
plot(pressure, col = thematic::thematic_get_option("accent"))
DT::datatable(datasets::anscombe)
g <- ggplot(mpg, aes(displ, hwy)) +
geom_point() +
geom_smooth()
plotly::ggplotly(g)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
library(echarts4r)
data <- expand.grid(
x = seq(-3, 3, by = 0.05),
y = seq(-3, 3, by = 0.05)
) |>
dplyr::mutate(z = sin(x * x + y * y) * x / 3.14)
data |>
e_charts(x) |>
e_surface(y, z, wireframe = list(show = FALSE)) |>
e_visual_map(z)
library(echarts4r)
les <- jsonlite::fromJSON("https://gist.githubusercontent.com/tyluRp/0d7a53f2a1f55cb3c6ffe22c67618267/raw/0684a839c3e49dac1157721ddd906eff8f9491d4/les-miserables.json")
e_charts() |>
e_graph(
layout = "circular",
circular = list(
rotateLabel = TRUE
),
roam = TRUE,
lineStyle = list(
color = "source",
curveness = 0.3
),
label = list(
position = "right",
formatter = "{b}"
)
) |>
e_graph_nodes(
nodes = les$nodes,
names = name,
value = value,
size = size,
category = grp
) |>
e_graph_edges(
edges = les$edges,
source = from,
target = to
) |>
e_tooltip()
library(kableExtra)
mtcars[1:5, 1:6] %>%
kbl() %>%
kable_styling()
| mpg | cyl | disp | hp | drat | wt | |
|---|---|---|---|---|---|---|
| Mazda RX4 | 21.0 | 6 | 160 | 110 | 3.90 | 2.620 |
| Mazda RX4 Wag | 21.0 | 6 | 160 | 110 | 3.90 | 2.875 |
| Datsun 710 | 22.8 | 4 | 108 | 93 | 3.85 | 2.320 |
| Hornet 4 Drive | 21.4 | 6 | 258 | 110 | 3.08 | 3.215 |
| Hornet Sportabout | 18.7 | 8 | 360 | 175 | 3.15 | 3.440 |
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
fig <- plot_ly(z = ~volcano, type = "contour")
fig
x <- c(1:100)
random_y <- rnorm(100, mean = 0)
data <- data.frame(x, random_y)
fig <- plot_ly(data, x = ~x, y = ~random_y, type = 'scatter', mode = 'lines')
fig
reactable::reactable(datasets::beaver1)
library(ggplot2)
library(ggiraph)
data <- mtcars
data$carname <- row.names(data)
gg_point = ggplot(data = data) +
geom_point_interactive(aes(x = wt, y = qsec, color = disp,
tooltip = carname, data_id = carname)) +
theme_minimal()
girafe(ggobj = gg_point)